Cuyama Valley micronet
The hypothesis tested is that shrubs directly and indirectly buffer local changes in the microenvironment thereby functioning as refuges for other species within arid and semi-arid regions subject to dramatic global change drivers. To examine this hypothesis for Santa Barbara County, the following predictions will be tested: (i) shrub micro-environments reduce the level of stress and amplitude of variation associated with temperature and moisture, (ii) many plant and animal species including threatened lizards are relatively more common with shrubs within the region, and (iii) the variation in the interaction patterns between species relates to the extent of amelioration provided by shrub-biodiversity complexes within the region.
macro-climate analyses: 1980-2005
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: (tmax^2)
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 149 378861
## site 5 80972 144 297889 2.223e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## $lsmeans
## site lsmean SE df asymp.LCL asymp.UCL
## site1 334.1493 9.096534 NA 316.3204 351.9781
## site2 334.9719 9.096534 NA 317.1430 352.8008
## site3 335.6182 9.096534 NA 317.7893 353.4470
## site4 330.8118 9.096534 NA 312.9829 348.6407
## site5 383.0701 9.096534 NA 365.2412 400.8990
## site6 383.0701 9.096534 NA 365.2412 400.8990
##
## Results are given on the identity (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## site1 - site2 -8.226311e-01 12.86444 NA -0.064 1.0000
## site1 - site3 -1.468900e+00 12.86444 NA -0.114 1.0000
## site1 - site4 3.337465e+00 12.86444 NA 0.259 0.9998
## site1 - site5 -4.892083e+01 12.86444 NA -3.803 0.0020
## site1 - site6 -4.892083e+01 12.86444 NA -3.803 0.0020
## site2 - site3 -6.462689e-01 12.86444 NA -0.050 1.0000
## site2 - site4 4.160096e+00 12.86444 NA 0.323 0.9995
## site2 - site5 -4.809820e+01 12.86444 NA -3.739 0.0025
## site2 - site6 -4.809820e+01 12.86444 NA -3.739 0.0025
## site3 - site4 4.806365e+00 12.86444 NA 0.374 0.9991
## site3 - site5 -4.745193e+01 12.86444 NA -3.689 0.0031
## site3 - site6 -4.745193e+01 12.86444 NA -3.689 0.0031
## site4 - site5 -5.225829e+01 12.86444 NA -4.062 0.0007
## site4 - site6 -5.225829e+01 12.86444 NA -4.062 0.0007
## site5 - site6 5.684342e-14 12.86444 NA 0.000 1.0000
##
## P value adjustment: tukey method for comparing a family of 6 estimates## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: (prcp^2)
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 149 2.3963e+12
## site 5 9.939e+10 144 2.2969e+12 0.2844## $lsmeans
## site lsmean SE df asymp.LCL asymp.UCL
## site1 135591.88 25259.44 NA 86084.29 185099.5
## site2 129289.52 25259.44 NA 79781.93 178797.1
## site3 86991.16 25259.44 NA 37483.57 136498.8
## site4 83612.04 25259.44 NA 34104.45 133119.6
## site5 72871.84 25259.44 NA 23364.25 122379.4
## site6 72871.84 25259.44 NA 23364.25 122379.4
##
## Results are given on the identity (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## site1 - site2 6.302360e+03 35722.24 NA 0.176 1.0000
## site1 - site3 4.860072e+04 35722.24 NA 1.361 0.7507
## site1 - site4 5.197984e+04 35722.24 NA 1.455 0.6930
## site1 - site5 6.272004e+04 35722.24 NA 1.756 0.4947
## site1 - site6 6.272004e+04 35722.24 NA 1.756 0.4947
## site2 - site3 4.229836e+04 35722.24 NA 1.184 0.8447
## site2 - site4 4.567748e+04 35722.24 NA 1.279 0.7968
## site2 - site5 5.641768e+04 35722.24 NA 1.579 0.6123
## site2 - site6 5.641768e+04 35722.24 NA 1.579 0.6123
## site3 - site4 3.379120e+03 35722.24 NA 0.095 1.0000
## site3 - site5 1.411932e+04 35722.24 NA 0.395 0.9988
## site3 - site6 1.411932e+04 35722.24 NA 0.395 0.9988
## site4 - site5 1.074020e+04 35722.24 NA 0.301 0.9997
## site4 - site6 1.074020e+04 35722.24 NA 0.301 0.9997
## site5 - site6 -7.275958e-11 35722.24 NA 0.000 1.0000
##
## P value adjustment: tukey method for comparing a family of 6 estimatesmacro-climate analyses: 2005-2015
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: (tmax^2)
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 65 398247
## site 5 37507 60 360740 0.2837## $lsmeans
## site lsmean SE df asymp.LCL asymp.UCL
## site1 336.3815 23.37896 NA 290.5596 382.2034
## site2 336.9029 23.37896 NA 291.0810 382.7248
## site3 338.5127 23.37896 NA 292.6908 384.3346
## site4 333.4139 23.37896 NA 287.5920 379.2358
## site5 386.7713 23.37896 NA 340.9494 432.5932
## site6 386.7713 23.37896 NA 340.9494 432.5932
##
## Results are given on the identity (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## site1 - site2 -5.214182e-01 33.06284 NA -0.016 1.0000
## site1 - site3 -2.131210e+00 33.06284 NA -0.064 1.0000
## site1 - site4 2.967581e+00 33.06284 NA 0.090 1.0000
## site1 - site5 -5.038978e+01 33.06284 NA -1.524 0.6487
## site1 - site6 -5.038978e+01 33.06284 NA -1.524 0.6487
## site2 - site3 -1.609792e+00 33.06284 NA -0.049 1.0000
## site2 - site4 3.488999e+00 33.06284 NA 0.106 1.0000
## site2 - site5 -4.986837e+01 33.06284 NA -1.508 0.6589
## site2 - site6 -4.986837e+01 33.06284 NA -1.508 0.6589
## site3 - site4 5.098791e+00 33.06284 NA 0.154 1.0000
## site3 - site5 -4.825857e+01 33.06284 NA -1.460 0.6901
## site3 - site6 -4.825857e+01 33.06284 NA -1.460 0.6901
## site4 - site5 -5.335737e+01 33.06284 NA -1.614 0.5893
## site4 - site6 -5.335737e+01 33.06284 NA -1.614 0.5893
## site5 - site6 -3.552714e-14 33.06284 NA 0.000 1.0000
##
## P value adjustment: tukey method for comparing a family of 6 estimates## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: (prcp^2)
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 65 6.8385e+11
## site 5 9.022e+09 60 6.7483e+11 0.9769## $lsmeans
## site lsmean SE df asymp.LCL asymp.UCL
## site1 106212.18 31976.08 NA 43540.21 168884.2
## site2 103852.91 31976.08 NA 41180.94 166524.9
## site3 79965.18 31976.08 NA 17293.21 142637.2
## site4 76105.09 31976.08 NA 13433.12 138777.1
## site5 83735.18 31976.08 NA 21063.21 146407.2
## site6 83735.18 31976.08 NA 21063.21 146407.2
##
## Results are given on the identity (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## site1 - site2 2.359273e+03 45221.01 NA 0.052 1.0000
## site1 - site3 2.624700e+04 45221.01 NA 0.580 0.9923
## site1 - site4 3.010709e+04 45221.01 NA 0.666 0.9856
## site1 - site5 2.247700e+04 45221.01 NA 0.497 0.9963
## site1 - site6 2.247700e+04 45221.01 NA 0.497 0.9963
## site2 - site3 2.388773e+04 45221.01 NA 0.528 0.9951
## site2 - site4 2.774782e+04 45221.01 NA 0.614 0.9901
## site2 - site5 2.011773e+04 45221.01 NA 0.445 0.9978
## site2 - site6 2.011773e+04 45221.01 NA 0.445 0.9978
## site3 - site4 3.860091e+03 45221.01 NA 0.085 1.0000
## site3 - site5 -3.770000e+03 45221.01 NA -0.083 1.0000
## site3 - site6 -3.770000e+03 45221.01 NA -0.083 1.0000
## site4 - site5 -7.630091e+03 45221.01 NA -0.169 1.0000
## site4 - site6 -7.630091e+03 45221.01 NA -0.169 1.0000
## site5 - site6 3.637979e-12 45221.01 NA 0.000 1.0000
##
## P value adjustment: tukey method for comparing a family of 6 estimates## prcp srad swe tmax tmin vp
## elevation 0.259915 0.437344 NA -0.9922931 -0.9905304 -0.8076309
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: AI
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 215 9011.4
## climate.data$site 5 225.69 210 8785.7 0.3696micro-climate analyses: 02/2016
## Linear mixed model fit by REML ['lmerMod']
## Formula: temp ~ microsite * site + (1 | month)
## Data: hobo.data
##
## REML criterion at convergence: 227074
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9129 -0.7858 -0.3540 0.7276 3.3358
##
## Random effects:
## Groups Name Variance Std.Dev.
## month (Intercept) 15.87 3.984
## Residual 140.93 11.871
## Number of obs: 29160, groups: month, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 16.54637 1.99898 8.277
## micrositeshrub 0.41504 0.22807 1.820
## site 0.27599 0.04841 5.701
## micrositeshrub:site 0.02210 0.06781 0.326
##
## Correlation of Fixed Effects:
## (Intr) mcrsts site
## microstshrb -0.061
## site -0.066 0.581
## mcrstshrb:s 0.048 -0.792 -0.713## Estimate Std..Error t.value temp.pvalue
## (Intercept) 16.54636582 1.99897625 8.2774199 2.220446e-16
## micrositeshrub 0.41503738 0.22807048 1.8197769 6.879299e-02
## site 0.27599492 0.04840759 5.7014808 1.187711e-08
## micrositeshrub:site 0.02210312 0.06781084 0.3259526 7.444603e-01## Linear mixed model fit by REML ['lmerMod']
## Formula: smc ~ microsite * site + (1 | month)
## Data: hobo.data
##
## REML criterion at convergence: -58783.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.5605 -0.4880 0.0353 0.6009 3.1000
##
## Random effects:
## Groups Name Variance Std.Dev.
## month (Intercept) 0.002737 0.05232
## Residual 0.007780 0.08820
## Number of obs: 29160, groups: month, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.1347090 0.0261901 5.144
## micrositeshrub 0.0089624 0.0016945 5.289
## site -0.0033538 0.0003597 -9.325
## micrositeshrub:site -0.0118808 0.0005038 -23.581
##
## Correlation of Fixed Effects:
## (Intr) mcrsts site
## microstshrb -0.035
## site -0.038 0.581
## mcrstshrb:s 0.027 -0.792 -0.713shrub morphology
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: shrub.size
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 179 2351.4
## site 1 829.16 178 1522.3 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: z
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 179 43.096
## site 1 4.6295 178 38.466 3.684e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1burrow patterns within Cuyama
## Analysis of Deviance Table
##
## Model: Negative Binomial(22.9769), link: log
##
## Response: burrows
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 35 154.853
## microsite 1 105.271 34 49.582 <2e-16 ***
## as.factor(site) 5 2.525 29 47.057 0.7728
## microsite:as.factor(site) 5 4.624 24 42.433 0.4634
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## $lsmeans
## microsite site lsmean SE df asymp.LCL asymp.UCL
## open 1 3.506558 0.1565481 NA 3.199729 3.813387
## shrub 1 2.538974 0.2020473 NA 2.142968 2.934979
## open 2 3.378725 0.1608445 NA 3.063475 3.693974
## shrub 2 2.268684 0.2213370 NA 1.834871 2.702496
## open 3 3.545779 0.1553148 NA 3.241367 3.850190
## shrub 3 2.036882 0.2408019 NA 1.564919 2.508845
## open 4 3.270836 0.1648197 NA 2.947795 3.593876
## shrub 4 2.484907 0.2056334 NA 2.081873 2.887941
## open 5 3.486355 0.1571986 NA 3.178252 3.794459
## shrub 5 2.512306 0.2037998 NA 2.112865 2.911746
## open 6 3.401197 0.1600576 NA 3.087490 3.714904
## shrub 6 2.079442 0.2370105 NA 1.614909 2.543974
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## open,1 - shrub,1 0.96758403 0.2555982 NA 3.786 0.0085
## open,1 - open,2 0.12783337 0.2244510 NA 0.570 1.0000
## open,1 - shrub,2 1.23787436 0.2711040 NA 4.566 0.0003
## open,1 - open,3 -0.03922071 0.2205221 NA -0.178 1.0000
## open,1 - shrub,3 1.46967597 0.2872158 NA 5.117 <.0001
## open,1 - open,4 0.23572233 0.2273166 NA 1.037 0.9969
## open,1 - shrub,4 1.02165125 0.2584423 NA 3.953 0.0044
## open,1 - open,5 0.02020271 0.2218529 NA 0.091 1.0000
## open,1 - shrub,5 0.99425227 0.2569857 NA 3.869 0.0061
## open,1 - open,6 0.10536052 0.2238878 NA 0.471 1.0000
## open,1 - shrub,6 1.42711636 0.2840445 NA 5.024 <.0001
## shrub,1 - open,2 -0.83975065 0.2582519 NA -3.252 0.0526
## shrub,1 - shrub,2 0.27029033 0.2996885 NA 0.902 0.9991
## shrub,1 - open,3 -1.00680474 0.2548447 NA -3.951 0.0045
## shrub,1 - shrub,3 0.50209194 0.3143385 NA 1.597 0.9105
## shrub,1 - open,4 -0.73186169 0.2607463 NA -2.807 0.1769
## shrub,1 - shrub,4 0.05406722 0.2882849 NA 0.188 1.0000
## shrub,1 - open,5 -0.94738132 0.2559971 NA -3.701 0.0116
## shrub,1 - shrub,5 0.02666825 0.2869799 NA 0.093 1.0000
## shrub,1 - open,6 -0.86222351 0.2577625 NA -3.345 0.0393
## shrub,1 - shrub,6 0.45953233 0.3114435 NA 1.475 0.9474
## open,2 - shrub,2 1.11004098 0.2736074 NA 4.057 0.0029
## open,2 - open,3 -0.16705408 0.2235926 NA -0.747 0.9999
## open,2 - shrub,3 1.34184260 0.2895799 NA 4.634 0.0002
## open,2 - open,4 0.10788896 0.2302965 NA 0.468 1.0000
## open,2 - shrub,4 0.89381788 0.2610671 NA 3.424 0.0304
## open,2 - open,5 -0.10763066 0.2249052 NA -0.479 1.0000
## open,2 - shrub,5 0.86641890 0.2596253 NA 3.337 0.0403
## open,2 - open,6 -0.02247286 0.2269127 NA -0.099 1.0000
## open,2 - shrub,6 1.29928298 0.2864349 NA 4.536 0.0004
## shrub,2 - open,3 -1.27709507 0.2703937 NA -4.723 0.0001
## shrub,2 - shrub,3 0.23180161 0.3270713 NA 0.709 0.9999
## shrub,2 - open,4 -1.00215202 0.2759631 NA -3.631 0.0149
## shrub,2 - shrub,4 -0.21622311 0.3021178 NA -0.716 0.9999
## shrub,2 - open,5 -1.21767165 0.2714801 NA -4.485 0.0005
## shrub,2 - shrub,5 -0.24362208 0.3008728 NA -0.810 0.9997
## shrub,2 - open,6 -1.13251384 0.2731456 NA -4.146 0.0020
## shrub,2 - shrub,6 0.18924200 0.3242901 NA 0.584 1.0000
## open,3 - shrub,3 1.50889668 0.2865454 NA 5.266 <.0001
## open,3 - open,4 0.27494305 0.2264691 NA 1.214 0.9880
## open,3 - shrub,4 1.06087196 0.2576971 NA 4.117 0.0023
## open,3 - open,5 0.05942342 0.2209844 NA 0.269 1.0000
## open,3 - shrub,5 1.03347299 0.2562363 NA 4.033 0.0032
## open,3 - open,6 0.14458123 0.2230272 NA 0.648 1.0000
## open,3 - shrub,6 1.46633707 0.2833667 NA 5.175 <.0001
## shrub,3 - open,4 -1.23395364 0.2918066 NA -4.229 0.0014
## shrub,3 - shrub,4 -0.44802472 0.3166554 NA -1.415 0.9610
## shrub,3 - open,5 -1.44947326 0.2875708 NA -5.040 <.0001
## shrub,3 - shrub,5 -0.47542370 0.3154678 NA -1.507 0.9391
## shrub,3 - open,6 -1.36431545 0.2891436 NA -4.718 0.0002
## shrub,3 - shrub,6 -0.04255961 0.3378751 NA -0.126 1.0000
## open,4 - shrub,4 0.78592891 0.2635349 NA 2.982 0.1136
## open,4 - open,5 -0.21551963 0.2277651 NA -0.946 0.9986
## open,4 - shrub,5 0.75852994 0.2621066 NA 2.894 0.1428
## open,4 - open,6 -0.13036182 0.2297476 NA -0.567 1.0000
## open,4 - shrub,6 1.19139402 0.2886859 NA 4.127 0.0022
## shrub,4 - open,5 -1.00144854 0.2588368 NA -3.869 0.0061
## shrub,4 - shrub,5 -0.02739897 0.2895159 NA -0.095 1.0000
## shrub,4 - open,6 -0.91629073 0.2605830 NA -3.516 0.0223
## shrub,4 - shrub,6 0.40546511 0.3137819 NA 1.292 0.9803
## open,5 - shrub,5 0.97404957 0.2573825 NA 3.784 0.0085
## open,5 - open,6 0.08515781 0.2243431 NA 0.380 1.0000
## open,5 - shrub,6 1.40691365 0.2844035 NA 4.947 <.0001
## shrub,5 - open,6 -0.88889176 0.2591385 NA -3.430 0.0298
## shrub,5 - shrub,6 0.43286408 0.3125833 NA 1.385 0.9666
## open,6 - shrub,6 1.32175584 0.2859937 NA 4.622 0.0002
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 12 estimates
## Tests are performed on the log scalevegetation patterns
## [1] all vegetation
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: veg$richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 215 388.55
## site 1 0.06240 214 388.48 0.8027
## microsite 1 0.90967 213 387.57 0.3402
## site:microsite 1 0.19238 212 387.38 0.6609
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: veg$total.density
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 215 573210
## site 1 12852.2 214 560357 0.023253 *
## microsite 1 21860.8 213 538497 0.003081 **
## site:microsite 1 9384.4 212 529112 0.052490 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] native and exotic species processed independently## Source: local data frame [6 x 4]
## Groups: site [3]
##
## site microsite species se
## (int) (fctr) (dbl) (dbl)
## 1 1 Open 0.5555556 0.04795503
## 2 1 Shrub 0.2222222 0.02910760
## 3 2 Open 0.7222222 0.06520270
## 4 2 Shrub 0.8333333 0.07471806
## 5 3 Open 1.0000000 0.07380124
## 6 3 Shrub 0.5000000 0.04207318
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: veg$richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 215 388.55
## site 1 0.06240 214 388.48 0.8027
## microsite 1 0.90967 213 387.57 0.3402
## site:microsite 1 0.19238 212 387.38 0.6609
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: veg$native.density
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 215 77971
## site 1 1456.77 214 76514 0.04184 *
## microsite 1 996.74 213 75518 0.09230 .
## site:microsite 1 950.91 212 74567 0.10013
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Source: local data frame [6 x 4]
## Groups: site [3]
##
## site microsite species se
## (int) (fctr) (dbl) (dbl)
## 1 1 Open 1.2222222 0.06415003
## 2 1 Shrub 0.8888889 0.06554983
## 3 2 Open 1.1111111 0.06125454
## 4 2 Shrub 1.1111111 0.06958048
## 5 3 Open 1.3888889 0.07431198
## 6 3 Shrub 1.4444444 0.08486251
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: veg$exotic.richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 215 257.39
## site 1 1.00827 214 256.38 0.3153
## microsite 1 0.01613 213 256.37 0.8989
## site:microsite 1 0.26333 212 256.10 0.6078
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: veg$exotic.density
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 215 463225
## site 1 5655.0 214 457570 0.09869 .
## microsite 1 13521.7 213 444049 0.01067 *
## site:microsite 1 4360.8 212 439688 0.14705
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: veg$prop.species.invaded
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 215 31.086
## site 1 0.50481 214 30.581 0.06126 .
## microsite 1 0.02971 213 30.552 0.64978
## site:microsite 1 0.00124 212 30.550 0.92614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: veg$prop.density.invaded
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 215 41.680
## site 1 0.086523 214 41.593 0.5065
## microsite 1 0.006609 213 41.587 0.8543
## site:microsite 1 0.020195 212 41.566 0.7483shrub-annual interactions
## site treatment total.density richness
## 1 1 clipped -1.000000000 -1.0000000
## 3 1 unclipped -0.983333333 -0.5000000
## 5 1 clipped -0.382198953 -0.1428571
## 7 1 unclipped -0.975609756 -0.5000000
## 9 1 clipped -1.000000000 -1.0000000
## 11 1 unclipped -0.750000000 -0.1428571
## 13 2 clipped -0.924528302 0.0000000
## 15 2 unclipped -0.894736842 0.0000000
## 17 2 clipped -0.600000000 -0.3333333
## 19 2 unclipped -0.473684211 -0.3333333
## 21 2 clipped -0.600000000 -0.3333333
## 23 2 unclipped -0.084337349 0.2000000
## 25 3 unclipped -0.263456091 -0.2000000
## 27 3 clipped 0.083056478 -0.1428571
## 29 3 unclipped 0.132075472 0.0000000
## 31 3 clipped -0.223140496 0.0000000
## 33 3 unclipped -0.583892617 0.0000000
## 35 3 clipped 0.022222222 0.1428571
## 37 4 clipped 0.469135802 0.3333333
## 39 4 unclipped -0.495495495 0.1428571
## 41 4 clipped -0.382716049 0.0000000
## 43 4 unclipped -0.632000000 0.0000000
## 45 4 clipped -0.057971014 0.0000000
## 47 4 unclipped 0.000000000 0.1428571
## 49 5 unclipped 0.510204082 -0.2000000
## 51 5 clipped -0.887323944 -0.3333333
## 53 5 unclipped -0.040816327 0.1428571
## 55 5 clipped 0.578947368 -0.6000000
## 57 5 unclipped -0.808510638 0.3333333
## 59 5 clipped 0.234567901 -0.2000000
## 61 6 unclipped -1.000000000 -1.0000000
## 63 6 clipped 0.120000000 0.0000000
## 65 6 unclipped -0.125000000 0.0000000
## 67 6 clipped -0.692307692 0.0000000
## 69 6 unclipped 0.185185185 0.0000000
## 71 6 clipped -0.333333333 0.0000000
## 73 1 clipped 0.000000000 0.0000000
## 75 1 unclipped -0.861111111 -0.2000000
## 77 1 clipped 0.000000000 0.0000000
## 79 1 unclipped 0.176470588 0.3333333
## 81 1 clipped 0.000000000 0.0000000
## 83 1 unclipped -0.909090909 -0.3333333
## 85 2 clipped 0.000000000 0.0000000
## 87 2 unclipped -0.643410853 0.2000000
## 89 2 clipped 1.000000000 1.0000000
## 91 2 unclipped -0.188284519 -0.1428571
## 93 2 clipped 0.000000000 0.0000000
## 95 2 unclipped 0.142857143 0.2500000
## 97 3 unclipped -0.490384615 -0.2000000
## 99 3 clipped 0.000000000 0.0000000
## 101 3 unclipped 0.580524345 0.0000000
## 103 3 clipped 0.000000000 0.0000000
## 105 3 unclipped -0.333333333 0.1428571
## 107 3 clipped 0.000000000 0.0000000
## 109 4 clipped 0.000000000 0.0000000
## 111 4 unclipped -0.395161290 0.2500000
## 113 4 clipped 0.000000000 0.0000000
## 115 4 unclipped -0.047619048 0.2000000
## 117 4 clipped 0.000000000 0.0000000
## 119 4 unclipped 0.336405530 -0.2000000
## 121 5 unclipped 0.231527094 0.2000000
## 123 5 clipped 0.000000000 0.0000000
## 125 5 unclipped -0.192052980 0.3333333
## 127 5 clipped 0.000000000 0.0000000
## 129 5 unclipped -0.934065934 -0.5000000
## 131 5 clipped 1.000000000 1.0000000
## 133 6 unclipped -1.000000000 -1.0000000
## 135 6 clipped 0.000000000 0.0000000
## 137 6 unclipped -0.733333333 -0.5000000
## 139 6 clipped 0.000000000 0.0000000
## 141 6 unclipped 0.370078740 0.2000000
## 143 6 clipped 0.000000000 0.0000000
## 145 1 clipped 0.000000000 0.0000000
## 147 1 unclipped -0.838709677 -0.2000000
## 149 1 clipped 0.000000000 0.0000000
## 151 1 unclipped -0.568627451 0.0000000
## 153 1 clipped 0.000000000 0.0000000
## 155 1 unclipped -0.534246575 -0.2000000
## 157 2 clipped 0.000000000 0.0000000
## 159 2 unclipped -0.609756098 -0.2500000
## 161 2 clipped 0.000000000 0.0000000
## 163 2 unclipped 0.355704698 0.0000000
## 165 2 clipped 1.000000000 1.0000000
## 167 2 unclipped -0.161290323 0.2500000
## 169 3 unclipped -1.000000000 -1.0000000
## 171 3 clipped 1.000000000 1.0000000
## 173 3 unclipped 0.007092199 -0.4285714
## 175 3 clipped 0.000000000 0.0000000
## 177 3 unclipped -0.439024390 0.0000000
## 179 3 clipped 0.000000000 0.0000000
## 181 4 clipped 0.000000000 0.0000000
## 183 4 unclipped -0.057471264 0.0000000
## 185 4 clipped 0.000000000 0.0000000
## 187 4 unclipped 0.031578947 0.3333333
## 189 4 clipped 0.000000000 0.0000000
## 191 4 unclipped -0.245614035 0.0000000
## 193 5 unclipped 0.120879121 -0.4285714
## 195 5 clipped 0.000000000 0.0000000
## 197 5 unclipped 0.229508197 0.0000000
## 199 5 clipped 0.000000000 0.0000000
## 201 5 unclipped -0.637583893 0.2500000
## 203 5 clipped 0.000000000 0.0000000
## 205 6 unclipped -0.941176471 -0.6000000
## 207 6 clipped 0.000000000 0.0000000
## 209 6 unclipped -0.459459459 0.2000000
## 211 6 clipped 0.000000000 0.0000000
## 213 6 unclipped 0.066666667 -0.1428571
## 215 6 clipped 0.000000000 0.0000000
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: rii.density
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 107 23.322
## site 1 0.56623 106 22.756 0.1044##
## One Sample t-test
##
## data: rii.veg$rii.density
## t = -4.0048, df = 107, p-value = 0.0001148
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.26896897 -0.09085533
## sample estimates:
## mean of x
## -0.1799122## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.0000 -0.5428 0.0000 -0.1799 0.0000 1.0000
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: rii.richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 107 13.070
## site 1 0.0052575 106 13.065 0.8364##
## One Sample t-test
##
## data: rii.veg$rii.richness
## t = -1.296, df = 107, p-value = 0.1978
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.11025300 0.02308369
## sample estimates:
## mean of x
## -0.04358466## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.00000 -0.15710 0.00000 -0.04358 0.00000 1.00000advanced climate-shrub-community interaction analyses
##
## Call:
## glm(formula = CVt ~ microsite * site, family = gaussian(), data = growing.season.means)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.25148 -0.03921 0.01307 0.06146 0.11890
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.80105 0.04576 17.506 2.9e-16 ***
## micrositeshrub -0.02804 0.06473 -0.433 0.668360
## site -0.04288 0.01127 -3.807 0.000737 ***
## micrositeshrub:site 0.01775 0.01608 1.104 0.279380
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.008629518)
##
## Null deviance: 0.40985 on 30 degrees of freedom
## Residual deviance: 0.23300 on 27 degrees of freedom
## AIC: -53.638
##
## Number of Fisher Scoring iterations: 2## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: CVt
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 30 0.40985
## microsite 1 0.010366 29 0.39949 0.2731
## site 1 0.155974 28 0.24351 2.124e-05 ***
## microsite:site 1 0.010516 27 0.23300 0.2696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = CVs ~ microsite * site, family = gaussian(), data = growing.season.means)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -6.6107 -1.3671 -0.2915 1.4558 6.7215
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7787 1.2577 3.004 0.00568 **
## micrositeshrub 0.7361 1.7791 0.414 0.68231
## site -0.3942 0.3096 -1.273 0.21381
## micrositeshrub:site -0.2432 0.4419 -0.550 0.58653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 6.51897)
##
## Null deviance: 213.25 on 30 degrees of freedom
## Residual deviance: 176.01 on 27 degrees of freedom
## AIC: 151.81
##
## Number of Fisher Scoring iterations: 2## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: CVs
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 30 213.25
## microsite 1 0.020 29 213.23 0.95531
## site 1 35.246 28 177.99 0.02006 *
## microsite:site 1 1.975 27 176.01 0.58200
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = temp ~ volume, data = hobo.means)
##
## Residuals:
## 1 2 3 4 5 6
## -1.5495 1.5373 0.5688 0.7040 -0.3850 -0.8756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.19082 0.84805 20.271 3.5e-05 ***
## volume -0.02663 0.08979 -0.297 0.782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.275 on 4 degrees of freedom
## Multiple R-squared: 0.02151, Adjusted R-squared: -0.2231
## F-statistic: 0.08793 on 1 and 4 DF, p-value: 0.7816##
## Call:
## lm(formula = smc ~ volume, data = hobo.means)
##
## Residuals:
## 1 2 3 4 5 6
## 0.117825 -0.066861 -0.029697 -0.008686 -0.001715 -0.010866
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.103286 0.046374 2.227 0.0899 .
## volume 0.001391 0.004910 0.283 0.7910
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0697 on 4 degrees of freedom
## Multiple R-squared: 0.01966, Adjusted R-squared: -0.2254
## F-statistic: 0.08024 on 1 and 4 DF, p-value: 0.791